
Gget
Fast Ensembl/BLAST-style genomic lookups and a reproducible evidence log before committing to Biopython, Snakemake, or local BLAST+ pipelines.
Overview
gget is an agent skill for the Validate phase that runs quick genomic database queries via CLI or Python and produces reproducible first-pass evidence logs.
Install
npx skills add https://github.com/affaan-m/everything-claude-code --skill ggetWhat is this skill?
- Single interface for Ensembl IDs, sequences, BLAST/BLAT, pathways, expression, and disease-association modules via gget
- Documented venv/uv install path with upgrade reminder because upstream databases change
- Explicit boundary: first-pass evidence log, not regulated clinical interpretation or high-throughput production
- Pairs CLI quick checks with Python package usage for reproducible notes
- When to use list covers metadata, reference links, and enrichment-style lookups without local indexes
Adoption & trust: 1.2k installs on skills.sh; 210k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need gene, sequence, or pathway facts from public genomic databases but do not yet want to build a full Snakemake, Nextflow, or local BLAST+ stack.
Who is it for?
Solo builders scoping a bio app, academic side projects, or agent-assisted literature-style lookups with reproducible command history.
Skip if: Regulated clinical diagnostics, production high-throughput pipelines, or workflows that require pinned local database indexes and fine-grained version control.
When should I use this skill?
Tasks need quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.
What do I get? / Deliverables
You get logged gget CLI or Python results you can attach to a scope or prototype decision, then graduate to heavier bioinformatics tools when the question is proven.
- Reproducible gget command or Python snippet transcript
- First-pass genomic evidence log for scope or prototype docs
Recommended Skills
Journey fit
Validate is where you prove a bioinformatics idea with lightweight queries and logged evidence before scaling infrastructure. Scope work here means bounding what genes, pathways, or references matter—not building the full production workflow yet.
How it compares
Lightweight query façade over public databases—not a replacement for Biopython pipelines, Snakemake, or self-hosted BLAST+.
Common Questions / FAQ
Who is gget for?
Indie builders and researchers who need fast genomic evidence from Ensembl and related modules without deploying a full bioinformatics cluster.
When should I use gget?
During Validate scoping when fetching Ensembl metadata, running quick BLAST/BLAT checks, pulling reference links, or drafting a reproducible evidence log before heavier Python or workflow tooling.
Is gget safe to install?
It installs a standard Python package that calls external databases over the network; confirm trust via the Security Audits panel on this page and keep API keys or patient data out of ad-hoc queries.
SKILL.md
READMESKILL.md - Gget
# gget Use this skill when a task needs quick bioinformatics lookup across genomic reference databases with the `gget` CLI or Python package. ## When to Use - Finding Ensembl IDs, gene metadata, transcript details, or sequences. - Running quick BLAST or BLAT lookups without building a full local pipeline. - Fetching reference genome links and annotations from Ensembl. - Querying protein structure, pathway, cancer, expression, or disease-association modules through a single interface. - Creating a reproducible first-pass evidence log before moving to heavier tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific clients. Use a dedicated workflow instead of `gget` when the task requires regulated clinical interpretation, high-throughput production pipelines, or fine-grained control over database versions and local indexes. ## Installation Use a clean Python environment. ```bash python -m venv .venv . .venv/bin/activate python -m pip install --upgrade pip python -m pip install --upgrade gget gget --help ``` If `uv` is available: ```bash uv venv . .venv/bin/activate uv pip install gget ``` Before relying on an older environment, upgrade `gget` and re-check the module docs. The upstream databases queried by `gget` change over time. ## Basic Patterns CLI shape: ```bash gget <module> [arguments] [options] ``` Python shape: ```python import gget result = gget.search(["BRCA1"], species="human") print(result) ``` Common workflow: 1. Identify the species, assembly, gene ID type, and database needed. 2. Check the current module documentation for arguments. 3. Run a small query first. 4. Save output with an explicit filename and date. 5. Record module name, version, arguments, and database assumptions. ## Common Modules Use current upstream docs for exact arguments. These modules are common first choices: - `gget search`: find Ensembl IDs from search terms. - `gget info`: retrieve metadata for Ensembl, UniProt, or related IDs. - `gget seq`: fetch nucleotide or amino-acid sequences. - `gget ref`: retrieve reference genome download links. - `gget blast`: run a quick BLAST query. - `gget blat`: locate a sequence against supported genome assemblies. - `gget muscle`: run multiple sequence alignment. - `gget diamond`: run local sequence alignment against reference sequences. - `gget alphafold` and `gget pdb`: inspect protein-structure references. - `gget enrichr`, `gget opentargets`, `gget archs4`, `gget bgee`, `gget cbio`, and `gget cosmic`: explore enrichment, target, expression, cancer, and disease association data. Do not assume every module supports every Python version or dependency set. Some optional scientific dependencies have narrower version support than the core package. ## Quick Examples Find genes: ```bash gget search -s human brca1 dna repair -o brca1-search.json ``` Fetch gene metadata: ```bash gget info ENSG00000012048 -o brca1-info.json ``` Fetch a sequence: ```bash gget seq ENSG00000012048 -o brca1-seq.fa ``` Run a small BLAST query: ```bash gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json ``` Python example: ```python import gget genes = gget.search(["BRCA1", "DNA repair"], species="human") info = gget.info(["ENSG00000012048"]) sequence = gget.seq("ENSG00000012048") ``` ## Reproducibility Log For scientific outputs, include enough metadata to replay the query. ```markdown | Date | gget version | Module | Query | Species/assembly | Output | Notes | | --- | --- | --- | --- | --- | --- | --- | | 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run | ``` Also record: - Python version and environment manager. - Any optional dependency installed through `gget setu